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dc.contributor.authorElshaikh, Abdallaen
dc.contributor.authorSalhi, Saiden
dc.contributor.authorBrimberg, Jacken
dc.contributor.authorMladenović, Nenaden
dc.contributor.authorCallaghan, Beckyen
dc.contributor.authorNagy, Gáboren
dc.date.accessioned2020-05-02T16:41:58Z-
dc.date.available2020-05-02T16:41:58Z-
dc.date.issued2016-11-01en
dc.identifier.issn0305-0548en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2423-
dc.description.abstractA self-adaptive heuristic that incorporates a variable level of perturbation, a novel local search and a learning mechanism is proposed to solve the p-centre problem in the continuous space. Empirical results, using several large TSP-Lib data sets, some with over 1300 customers with various values of p, show that our proposed heuristic is both effective and efficient. This perturbation metaheuristic compares favourably against the optimal method on small size instances. For larger instances the algorithm outperforms both a multi-start heuristic and a discrete-based optimal approach while performing well against a recent powerful VNS approach. This is a self-adaptive method that can easily be adopted to tackle other combinatorial/global optimisation problems. For benchmarking purposes, the medium size instances with 575 nodes are solved optimally for the first time, though requiring a large amount of computational time. As a by-product of this research, we also report for the first time the optimal solution of the vertex p-centre problem for these TSP-Lib data sets.en
dc.publisherElsevier-
dc.relationUK Research Council EPSRC (EP/I009299/1)-
dc.relationNatural Sciences & Engineering Research Council of Canada Discovery Grant (NSERC #20541 – 2008)-
dc.relationRussian Federation Grant RFS 14-41-00039-
dc.relationNational Council for Scientific and Technological Development – CNPq/Brazil grant number 400350/2014-9-
dc.relationSpanish Ministry of Economy and Competitiveness, research project MTM2015-70260-P-
dc.relation.ispartofComputers and Operations Researchen
dc.subjectAdaptive search | Continuous space | Large instances | Optimal solutions | p-centre problem | Perturbation searchen
dc.titleAn adaptive perturbation-based heuristic: An application to the continuous p-centre problemen
dc.typeArticleen
dc.identifier.doi10.1016/j.cor.2016.04.018en
dc.identifier.scopus2-s2.0-84969802519en
dc.relation.firstpage1en
dc.relation.lastpage11en
dc.relation.volume75en
dc.description.rankM21-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0001-6655-0409-
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